1Machine VisionSUMMER ACADEMY 2004

by

Maya akmak

Middle East Technical University

Electrical and Electronics Engineering

2VISION

the most powerful sense

3OUTLINE

1. Properties of Machine Vision Systems

Methods in Machine Vision

2. Example: Fingerprint Recognition

System

4Machine Vision System

creates a model of the real world from images

recovers useful information about a scene from its two dimensional

projections

5A typical control system:

Scene Image Description

Application

feedback

Imaging

device

MACHINE

VISION

Illumination

6Components of a M.V. system

HARDWARE

SOFTWARE Optics (lenses, lighting)

Cameras

Interface (frame grabber)

Computer

7Components of a M.V. system

HARDWARE

SOFTWARE

-There is no universal vision system-One system for each application

Libraries with custom code developed using Visual C/C++, Visual Basic, or Java

Graphical programming environments

8Application Fields

Medical imaging

Industrial automation

Robotics

Radar Imaging

Forensics

Remote Sensing

Cartography

Character recognition

9Machine Vision Stages

Image Acquisition

Image Processing

Image Segmentation

Image Analysis

Pattern Recognition

Analog to digital conversion

Remove noise,

improve contrast

Find regions (objects)

in the image

Take measurements of

objects/relationships

Match the description with

similar description of known

objects (models)

10

Image Formation

Perspective projection

Orthographic projection

3D 2DProjection

11

Digital Image Representation

Image: 2D array of gray level or color values

Pixel: Array element Picture Element

It`s 2DIt`s a squareIt has one colorIt may be any color

Pixel value: Arithmetic value of gray level or color intensity

Gray level image: f = f(x,y)

Color image: f = {Rred(x,y), Ggreen(x,y), Bblue(x,y)}

12

Digital Image Formation

Sampling rate

(resolution)

Quantization level

Sampling Quantization

13

Different Sampling Rates

14

Different Quantization Levels

15

Image Processing (IP)

Input Image Output Image

Image Processing

Filtering

Smoothing

Thinning

Expending

Shrinking

Compressing

16

Fundementals Neigborhood

Histogram: gray levels vs number of pixels

4-Neighbors 8-Neighbors

17

IP Examples

Removal of noise with median filter

18

IP Examples (2)

Improvement of contrast by histogram modification

19

Symethrical

inversion of the image

IP Examples

(3)

20

IP Examples (4)

Smoothing

Original Uniform Gaussian

21

Binary Image ProcessingWHY?

better efficiency in acquiring, storage,

processing and transmission

TRESHOLDING

22

Different tresholds

23

Image Segmentation

Input ImageRegionsObjects

Segmentation

-Clasify pixels into groups having similar characteristics

-Two techniques: Region segmentation

Edge detection

24

Region Segmentation

Histogram Based

25

Region Segmentation (2)

Split & Merge

26

Edge Detection

Find the curves on the image where

rapid changes occur

27

Image Analysis

Input ImageRegions, objects

Measurements

Image Analysis

Measurements:

-Size

-Position

-Orientation

-Spatial relationship

-Gray scale or color intensity

-Velocity

28

Pattern Recognition (PR)

- Measurements

- Stuctural

descriptions

Class identifier

Pattern

Recognition

feature vector

set of information data

29

Approaches to PR

Statistical

Structural

Neural

30

3D VISION

Dynamic Vision

31

Fingerprint Recognition

- most precise identification

biometric

- has many applications

- has the largest database

32

33

Fingerprint recognition system

Fingerprint

sensor

Fingerprint

sensor

Feature Extractor

Feature Extractor

Feature Matcher

ID

Enrollment

Identification

Template

database

34

Fingerprint Representation

Local Ridge Characteristics

Ridge EndingRidge BifurcationEnclosureRidge Dot

35

Fingerprint Representation (2)

Singular Points

Core

Delta

Major Central Pattern

Arch Loop Whorl

36

Image Processing & Analysis for

Fingerprint Recognition

Pre-processing Minutiae

ExtractionPost-processing

Input Image

Binarization

Noise Removal

Smoothing

Thinning

Ridge break removal

Bridge removal

Spike removal

Processed

Image

+

Minutiae

Description

Outputs

37

Pre-Processing

Binarizationsearch through array pixel by pixel;

if current byte colour value is above threshold then

change value to white;

else

change colour to black

Noise Removal if the pixel is white and all immediate surrounding pixels are

black

then

change pixel to black;

else if the pixel is black and all immediate surrounding pixels

are white then

change pixel to white;

38

Pre-Processing(2)

Smoothingfor each pixel do

add the values of surrounding pixels;

divide by the number of surrounding pixels (usually 8 unless

at the edge);

assign current pixel the calculated result;

Thinning

39

Extraction

if pixel has exactly one neighbour then

pixel is a ridge ending;

calculate x value of pixel;

calculate y value of pixel;

calculate angle of ridge ending;

write data to database;

else if pixel has exactly three neighbours then

pixel is a bifurcation;

calculate x value of pixel;

calculate y value of pixel;

calculate angle of bifurcation;

write data to database;

40

Post-Processing Ridge break removalIf A and B are facing each other and are less then a minimum

distance (D) then

A and B are false minutia points;

remove A and B from ridge end point set;

update database;

Bridge removal

Spike removalFor each ridge ending point (A)

For each bifurcation point (B)

If A and B are less than minimum distance (D)apart

and they are connected then

A and B are false minutia points;

remove A and B from their sets;

41

Matching

42

References[1] MACHINE VISION

Jain-Kasturi-Schunck

[2] EE 701 Robot Vision Lecture Notes

A. Aydin Alatan METU

[3] Tutorial on Machine Vision

Petrakis

[4] Multimodel User Interfaces

Anil Jain

[5] Automatic Fingerprint Recognition System

Tony Walsh

43

THANK YOU!